Diagnostic Performance of Fractional Flow Reserve From CT Coronary Angiography With Analytical Method.

The aim of this study was to evaluate a new analytical method for calculating non-invasive fractional flow reserve (FFRAM) to diagnose ischemic coronary lesions. Patients with suspected or known coronary artery disease (CAD) who underwent computed tomography coronary angiography (CTCA) and invasive coronary angiography (ICA) with FFR measurements from two sites were prospectively recruited. Obstructive CAD was defined as diameter stenosis (DS) ≥50% on CTCA or ICA. FFRAM was derived from CTCA images and anatomical features using analytical method and was compared with computational fluid dynamics (CFD)-based FFR (FFRB) and invasive ICA-based FFR. FFRAM, FFRB, and invasive FFR ≤ 0.80 defined ischemia. A total of 108 participants (mean age 60, range: 30-83 years, 75% men) with 169 stenosed coronary arteries were analyzed. The per-vessel accuracy, sensitivity, specificity, and positive predictive and negative predictive values were, respectively, 81, 75, 86, 81, and 82% for FFRAM and 87, 88, 86, 83, and 90% for FFRB. The area under the receiver operating characteristics curve for FFRAM (0.89 and 0.87) and FFRB (0.90 and 0.86) were higher than both CTCA- and ICA-derived DS (all p < 0.0001) on per-vessel and per-patient bases for discriminating ischemic lesions. The computational time for FFRAM was much shorter than FFRB (2.2 ± 0.9 min vs. 48 ± 36 min, excluding image acquisition and segmentation). FFRAM calculated from a novel and expeditious non-CFD approach possesses a comparable diagnostic performance to CFD-derived FFRB, with a significantly shorter computational time.

Computed Tomography Coronary Angiography and Computational Fluid Dynamics Based Fractional Flow Reserve Before and After Percutaneous Coronary Intervention.

Invasive fractional flow reserve (FFR) is recommended to guide stent deployment. We previously introduced a non-invasive FFR calculation (FFRB) based on computed tomography coronary angiography (CTCA) with reduced-order computational fluid dynamics (CFD) and resistance boundary conditions. Current study aimed to assess the feasibility and accuracy of FFRB for predicting coronary hemodynamics before and after stenting, with invasive FFR as the reference. Twenty-five patients who had undergone CTCA were prospectively enrolled before invasive coronary angiography (ICA) and FFR-guided percutaneous coronary intervention (PCI) on 30 coronary vessels. Using reduced-order CFD with novel boundary conditions on three-dimensional (3D) patient-specific anatomic models reconstructed from CTCA, we calculated FFRB before and after virtual stenting. The latter simulated PCI by clipping stenotic segments from the 3D coronary models and replacing them with segments to mimic the deployed coronary stents. Pre- and post-virtual stenting FFRB were compared with FFR measured pre- and post-PCI by investigators blinded to FFRB results. Among 30 coronary lesions, pre-stenting FFRB (mean 0.69 ± 0.12) and FFR (mean 0.67 ± 0.13) exhibited good correlation (r = 0.86, p < 0.001) and agreement [mean difference 0.024, 95% limits of agreement (LoA): -0.11, 0.15]. Similarly, post-stenting FFRB (mean 0.84 ± 0.10) and FFR (mean 0.86 ± 0.08) exhibited fair correlation (r = 0.50, p < 0.001) and good agreement (mean difference 0.024, 95% LoA: -0.20, 0.16). The accuracy of FFRB for identifying post-stenting ischemic lesions (FFR ≤ 0.8) (residual ischemia) was 87% (sensitivity 80%, specificity 88%). Our novel FFRB, based on CTCA with reduced-order CFD and resistance boundary conditions, accurately predicts the hemodynamic effects of stenting which may serve as a tool in PCI planning.

Quantification of effects of mean blood pressure and left ventricular mass on noninvasive fast fractional flow reserve.

Proper inlet boundary conditions are essential for accurate computational fluid dynamics (CFD) modeling. We developed methodology to derive noninvasive FFRB using CFD and computed tomography coronary angiography (CTCA) images. This study aims to assess the influence of brachial mean blood pressure (MBP) and total coronary inflow on FFRB computation. Twenty-two patients underwent both CTCA and FFR measurements. Total coronary flow was computed from left ventricular mass (LVM) measured from CTCA. A total of 286 CFD simulations were run by varying MBP and LVM at 70, 80, 90, 100, 110, 120, and 130% of the measured values. FFRB increased with incrementally higher input values of MBP: 0.78 ± 0.12, 0.80 ± 0.11, 0.82 ± 0.10, 0.84 ± 0.09, 0.85 ± 0.08, 0.86 ± 0.08, and 0.87 ± 0.07, respectively. Conversely, FFRB decreased with incrementally higher inputs value of LVM: 0.86 ± 0.08, 0.85 ± 0.08, 0.84 ± 0.09, 0.84 ± 0.09, 0.83 ± 0.10, 0.83 ± 0.10, and 0.82 ± 0.10, respectively. Noninvasive FFRB calculated using measured MBP and LVM on a total of 30 vessels was 0.84 ± 0.09 and correlated well with invasive FFR (0.83 ± 0.09) (r = 0.92, P < 0.001). Positive association was observed between FFRB and MBP input values (mmHg) and negative association between FFRB and LVM values (g). Respective slopes were 0.0016 and -0.005, respectively, suggesting potential application of FFRB in a clinical setting. Inaccurate MBP and LVM inputs differing from patient-specific values could result in misclassification of borderline ischemic lesions.NEW & NOTEWORTHY While brachial mean blood pressure (MBP) and left ventricular mass (LVM) measured from CTCA are the two CFD simulation input parameters, their effects on noninvasive fractional flow reserve (FFRB) have not been systematically investigated. We demonstrate that inaccurate MBP and LVM inputs differing from patient-specific values could result in misclassification of borderline ischemic lesions. This is important in the clinical application of noninvasive FFR in coronary artery disease diagnosis.

Reference Ranges for Left Ventricular Curvedness and Curvedness-Based Functional Indices Using Cardiovascular Magnetic Resonance in Healthy Asian Subjects.

Curvature-based three-dimensional cardiovascular magnetic resonance (CMR) allows regional function characterization without an external spatial frame of reference. However, introduction of this modality into clinical practice is hampered by lack of reference values. We aim to establish normal ranges for 3D left ventricular (LV) regional parameters in relation to age and gender for 171 healthy subjects. LV geometrical reconstruction and automatic calculation of regional parameters were implemented by in-house software (CardioWerkz) using stacks of short-axis cine slices. Parameter normal ranges were stratified by gender and age categories (≤44, 45-64, 65-74 and 75-84 years). Our software had excellent intra- and inter-observer agreement. Ageing was significantly associated with increases in end-systolic (ES) curvedness (CES) and area strain (AS) with higher rates of increase in males, end-diastolic (ED) and ES wall thickness (WTED, WTES) with higher rates of increase in females, and reductions in ED and ES wall stress indices (σi,ED) with higher rates of increase in females. Females exhibited greater ED curvedness, CES, σi,ED and AS than males, but smaller WTED and WTES. Age × gender interaction was not observed for any parameter. This study establishes age and gender specific reference values for 3D LV regional parameters using CMR without additional image acquisition.

Advanced analyses of computed tomography coronary angiography can help discriminate ischemic lesions.

BACKGROUND: Computed tomography coronary angiography (CTCA) image analysis enables plaque characterization and non-invasive fractional flow reserve (FFR) calculation. We analyzed various parameters derived from CTCA images and evaluated their associations with ischemia. METHODS: 49 (61 lesions) patients underwent CTCA and invasive FFR. Lesions with diameter stenosis (DS) ≥ 50% were considered obstructive. CTCA image processing incorporating analytical and numerical methods were used to quantify anatomical parameters of lesion length (LL) and minimum lumen area (MLA); plaque characteristic parameters of plaque volume, low attenuation plaque (LAP) volume, dense calcium volume (DCV), normalized plaque volume (NP Vol), plaque burden, eccentricity index and napkin-ring (NR) sign; and hemodynamic parameters of resistance index, stenosis flow reserve (SFR) and FFRB. Ischemia was defined as FFR ≤ 0.8. RESULTS: Plaque burden and plaque volume were inversely related to FFR. Multivariable logistic regression analysis identified the best anatomical, plaque and hemodynamic predictors, respectively, as DS (≥50% vs <50%; OR: 8.0; 95% CI: 1.6-39.4), normalized plaque volume (NP Vol) (≥4.3 vs <4.3; OR: 3.9; 95% CI: 1.1-14.0) and NR Sign (0 vs 1; OR: 13.6; 95% CI: 1.3-146.1), and FFRB (≤0.8 vs >0.8; OR: 44.4; 95% CI: 8.8-224.8). AUC increased from 0.70 with DS as the sole predictor to 0.81 after adding NP Vol and NR Sign; further addition of FFRB increased AUC to 0.93. CONCLUSION: Normalized plaque volume, napkin-ring derived from plaque analysis, and FFRB from numerical simulations on CTCA images substantially improved discrimination of ischemic lesions, compared to assessment by DS alone.